**Typhoon-8B** is a *pretrained only* Thai 🇹🇭 large language model with 8 billion parameters, and it is based on Llama3-8B. For release notes, please see our blog. *To acknowledge Meta's effort in creating the foundation model and to comply with the license, we explicitly include "llama-3" in the model name. This model is a pretrained base model. Thus, it may not be able to follow human instructions without using one/few-shot learning or instruction fine-tuning. The model does not have any moderation mechanisms, and may generate harmful or inappropriate responses.
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**Llama-3-Typhoon-1.5-8B-instruct** is a *instruct* Thai 🇹🇭 large language model with 8 billion parameters, and it is based on Llama3-8B. For release post, please see our blog. *To acknowledge Meta's effort in creating the foundation model and to comply with the license, we explicitly include "llama-3" in the model name. This model is an instructional model. However, it’s still undergoing development. It incorporates some level of guardrails, but it still may produce answers that are inaccurate, biased, or otherwise objectionable in response to user prompts. We recommend that developers assess these risks in the context of their use case.
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**Llama3.1-Typhoon2-8B-instruct** is a instruct Thai 🇹🇭 large language model with 8 billion parameters, and it is based on Llama3.1-8B. For technical-report. please see our arxiv. *To acknowledge Meta's effort in creating the foundation model and to comply with the license, we explicitly include "llama-3.1" in the model name. This model is an instructional model. However, it’s still undergoing development. It incorporates some level of guardrails, but it still may produce answers that are inaccurate, biased, or otherwise objectionable in response to user prompts. We recommend that developers assess these risks in the context of their use case.
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**Typhoon2-Audio** is an end-to-end speech-to-speech model architecture capable of processing audio, speech, and text inputs and generating both text and speech outputs simultaneously. It is optimized specifically for Thai, but it also supports English. - **GitHub**: https://github.com/scb-10x/typhoon2-audio/ - **Paper**: https://arxiv.org/abs/2412.13702 - **Model type**: The LLM is based on Typhoon2 LLM. - **Requirement**: Python==3.10 & transformers==4.52.2 & fairseq==0.12.2 & flash-attn - **Primary Language(s)**: Thai 🇹🇭 and English 🇬🇧 - **License-Speech-Input & LLM**: Llama 3.1 Community License - **License-Speech-Output**: CC-BY-NC
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Thai ASR with **prompt-based contextual biasing**: a LoRA fine-tune (merged, 0.80B params) of `Qwen/Qwen3-ASR-0.6B-hf` that accepts a list of names/terms in the system prompt, boosts the ones that are actually spoken, writes them with the **listed spelling**, and ignores the rest (distractor false alarm rate ≤.0004; non-speech probe 5/5). The economical tier: 2.6× smaller than the 1.7b for ~.09 recall. Plain transcription (CER%, same protocol: gigaspeech2 bench in-domain / TVSpeech out-of-domain 30-s talk-show): † single-digit repetition-loop utterances dominate (0.6b: one utt; Thonburian: three, excluding them 8.39).
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**Typhoon-OCR-3B**: A bilingual document parsing model built specifically for real-world documents in Thai and English inspired by models like olmOCR based on Qwen2.5-VL-Instruction. By using this model, you agree to the OpenTyphoon Terms and Conditions and acknowledge the Privacy Notice: https://opentyphoon.ai/tac · https://opentyphoon.ai/privacy **Code / Examples available on Github**
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A Smaller, More Robust, and Faster Vision-Language OCR for Thai Real-World Documents We’re thrilled to announce Typhoon OCR v1.5, the next evolution of our open-source vision-language document parsing model for English and Thai. Built on top of Qwen3-VL 2B, this release delivers faster inference, improved understanding of handwritten and form-based documents, and enhanced handling of both text-rich and image-rich pages—all in a smaller, more efficient package. By using this model, you agree to the OpenTyphoon Terms and Conditions and acknowledge the Privacy Notice: https://opentyphoon.ai/tac · https://opentyphoon.ai/privacy **Code / Examples available on Github**
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**Typhoon Whisper Large v3** is a state-of-the-art Thai Automatic Speech Recognition (ASR) model fine-tuned on the OpenAI Whisper Large v3 architecture. It delivers exceptional accuracy on Thai speech recognition tasks, achieving superior performance through comprehensive training on diverse Thai audio data. By using this model, you agree to the OpenTyphoon Terms and Conditions and acknowledge the Privacy Notice: https://opentyphoon.ai/tac · https://opentyphoon.ai/privacy The model was trained on approximately **10 million data points** (~11,000 hours) of Thai audio, curated and normalized using the Typhoon data pipeline to ensure consistent handling of Thai numbers, repetition markers, and context-dependent ambiguities.
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Word- and character-level **timestamps for Thai**: a 29M character-CTC head on the **frozen** `typhoon-ai/typhoon-whisper-large-v3` encoder (pinned revision `748e8a4`, recorded in `config.json`). One encoder pass gives transcription *and* alignment. It is **both an aligner and a compact ASR model**: it force-aligns given text (a transcript or ASR hypothesis) to word/character timestamps, and its greedy CTC decode transcribes directly (`--transcribe`, no text needed). `standalone_align.py` (bundled) has no other dependencies; the frozen encoder downloads automatically at the pinned revision. Files: `head.safetensors`, `config.json` (architecture + encoder id/revision), `ctc_vocab.json` (80-symbol Thai char vocab v1.0.0).
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Thai Whisper-medium with contextual biasing support via a decoder LoRA. Merged base weights + adapter in one repo. Full fine-tune of `openai/whisper-medium` on ~10,846 hours of Thai speech (see the Typhoon ASR Real-time technical report). `bias@0` = gold entities only (an oracle upper bound). `bias@10` = gold plus 10 distractors, the realistic condition. `dis@10` = distractors only, measuring over-biasing.
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**Typhoon Whisper Turbo** is a state-of-the-art Thai Automatic Speech Recognition (ASR) model fine-tuned on the OpenAI Whisper Large v3 Turbo architecture. It is engineered to deliver a balance of high accuracy and low latency for offline transcription, significantly outperforming standard Whisper models in throughput while maintaining robust performance on Thai speech. The model was presented in the paper Typhoon ASR Real-time: FastConformer-Transducer for Thai Automatic Speech Recognition. By using this model, you agree to the OpenTyphoon Terms and Conditions and acknowledge the Privacy Notice: https://opentyphoon.ai/tac · https://opentyphoon.ai/privacy
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